Table of Contents

• Applying some EDAs and hybrid variants to the ERM problem under uncertaintyYoan Martínez López, Ansel Y. Rodríguez González, Julio Madera Quintana, Miguel Bethencourt Mayedo, Alexis Moya, Oscar Martínez Santiago. 1-2 [doi]
• Weighting-based parallel local search for optimal camera placement and unicost set coveringWeibo Lin, Fuda Ma, Zhouxing Su, Qingyun Zhang, Chumin Li, Zhipeng Lü. 3-4 [doi]
• A weighted population update rule for PACO applied to the single machine total weighted tardiness problemDaniel Abitz, Tom Hartmann, Martin Middendorf. 4-12 [doi]
• Deep statistics: more robust performance statistics for single-objective optimization benchmarkingTome Eftimov, Gasper Petelin, Rok Hribar, Gorjan Popovski, Urban Skvorc, Peter Korosec. 5-6 [doi]
• PerformViz: a machine learning approach to visualize and understand the performance of single-objective optimization algorithmsTome Eftimov, Rok Hribar, Urban Skvorc, Gorjan Popovski, Gasper Petelin, Peter Korosec. 7-8 [doi]
• SOMA-CL for competition on single objective bound constrained numerical optimization benchmark: a competition entry on single objective bound constrained numerical optimization at the genetic and evolutionary computation conference (GECCO) 2020Tomas Kadavy, Michal Pluhacek, Adam Viktorin, Roman Senkerik. 9-10 [doi]
• A modified covariance matrix adaptation evolution strategy for real-world constrained optimization problemsAbhishek Kumar, Swagatam Das, Ivan Zelinka. 11-12 [doi]
• A self-adaptive spherical search algorithm for real-world constrained optimization problemsAbhishek Kumar, Swagatam Das, Ivan Zelinka. 13-14 [doi]
• ACO with automatic parameter selection for a scheduling problem with a group cumulative constraintLucas Groleaz, Samba Ndojh Ndiaye, Christine Solnon. 13-21 [doi]
• Sharp bounds for genetic drift in estimation of distribution algorithms (Hot-off-the-press track at GECCO 2020)Benjamin Doerr, Weijie Zheng. 15-16 [doi]
• The univariate marginal distribution algorithm copes well with deception and epistasisBenjamin Doerr, Martin S. Krejca. 17-18 [doi]
• Is the statistical significance between stochastic optimization algorithms' performances also significant in practice?Tome Eftimov, Peter Korosec. 19-20 [doi]
• Large scale biomedical data analysis with tree-based automated machine learningTrang T. Le, Weixuan Fu, Jason H. Moore. 21-22 [doi]
• A multiobjective optimization approach for market timingIsmail Mohamed, Fernando E. B. Otero. 22-30 [doi]
• Empirical linkage learningMichal Witold Przewozniczek, Marcin M. Komarnicki. 23-24 [doi]
• SGP-DT: towards effective symbolic regression with a semantic GP approach based on dynamic targetsStefano Ruberto, Valerio Terragni, Jason H. Moore. 25-26 [doi]
• Using exploratory landscape analysis to visualize single-objective problemsUrban Skvorc, Tome Eftimov, Peter Korosec. 27-28 [doi]
• Combined kriging surrogate model for efficient global optimization using the optimal weighting methodTanguy Appriou, Koji Shimoyama. 29-30 [doi]
• An approach to assess swarm intelligence algorithms based on complex networksClodomir Santana, Edward Keedwell, Ronaldo Menezes. 31-39 [doi]
• A daily stock index predictor using feature selection based on a genetic wrapperDong-Hee Cho, Seung-Hyun Moon, Yong-Hyuk Kim. 31-32 [doi]
• A genetic algorithm to optimize SMOTE and GAN ratios in class imbalanced datasetsHwi-Yeon Cho, Yong-Hyuk Kim. 33-34 [doi]
• Data driven building of realistic neuron model using IBEA and CMA evolution strategiesTanguy Damart, Werner Van Geit, Henry Markram. 35-36 [doi]
• A bio-inspired approach for the spectrum allocation problem in IoT networksJesús A. Gómez-Avilés, Ángel G. Andrade, Anabel Martínez-Vargas. 37-38 [doi]
• A surrogate model using deep neural networks for optimal oil skimmer assignmentHye-Jin Kim, Yong-Hyuk Kim. 39-40 [doi]
• Exploratory path planning for mobile robots in dynamic environments with ant colony optimizationValéria de Carvalho Santos, Fernando E. B. Otero, Colin G. Johnson, Fernando Santos Osório, Cláudio Fabiano Motta Toledo. 40-48 [doi]
• Usage of a genetic algorithm for optimizing stock usageMateus Interciso, Plínio Barrio Garcia. 41-42 [doi]
• Automatic evolutionary learning of composite models with knowledge enrichmentAnna V. Kalyuzhnaya, Nikolay O. Nikitin, Pavel Vychuzhanin, Alexander Hvatov, Alexander Boukhanovsky. 43-44 [doi]
• An RTS-based algorithm for noisy optimization by strategic sample accumulationJeongmin Kim, Kwang Ryel Ryu. 45-46 [doi]
• Teng-Yue algorithm: a novel metaheuristic search method for fast cancer classificationTengyue Li, Simon Fong, Antonio J. Tallón-Ballesteros. 47-48 [doi]
• Constraint handling in genotype to phenotype mapping and genetic operators for project staffingSoo Ling Lim, Yi Kuo, Peter J. Bentley. 49-50 [doi]
• Learning behaviour-performance maps with meta-evolutionDavid M. Bossens, Jean-Baptiste Mouret, Danesh Tarapore. 49-57 [doi]
• Antimander: open source detection of gerrymandering though multi-objective evolutionary algorithmsJoel Simon, Joel Lehman. 51-52 [doi]
• Towards improvement of SUNA in multiplexers with preliminary results of simple logic gate neuron variationAnh Due Ta, Danilo Vasconcellos Vargas. 53-54 [doi]
• Quality enhancement of stochastic feature subset selection via genetic algorithms. Assessment in bioinformatics data setsAntonio J. Tallón-Ballesteros, Tengyue Li, Simon Fong. 55-56 [doi]
• On the order of variables for multitasking optimizationLei Wang, Qian Sun, Qingzheng Xu, Balin Tian, Wei Li. 57-58 [doi]
• Diversity preservation in minimal criterion coevolution through resource limitationJonathan C. Brant, Kenneth O. Stanley. 58-66 [doi]
• An effective variable transfer strategy in multitasking optimizationQingzheng Xu, Balin Tian, Lei Wang, Qian Sun, Feng Zou. 59-60 [doi]
• On the co-authorship network in evolutionary computationDong-Pil Yu, Yong-Hyuk Kim. 61-62 [doi]
• Evolutionary curriculum learning approach for transferable cellular automata rule optimizationDelia Dumitru, Anca Andreica, Laura Diosan, Zoltán Bálint. 63-64 [doi]
• n problemJudhi Prasetyo, Giulia De Masi, Elio Tuci, Eliseo Ferrante. 65-66 [doi]
• Scaling MAP-Elites to deep neuroevolutionCédric Colas, Vashisht Madhavan, Joost Huizinga, Jeff Clune. 67-75 [doi]
• Multi-objective ant colony optimization for task allocation in vehicle-based crowdsourcingJian Shi, Wei-neng Chen. 67-68 [doi]
• Improving BPSO-based feature selection applied to offline WI handwritten signature verification through overfitting controlVictor L. F. Souza, Adriano L. I. Oliveira, Rafael M. O. Cruz, Robert Sabourin. 69-70 [doi]
• Evolving hypernetworks for game-playing agentsChristian Carvelli, Djordje Grbic, Sebastian Risi. 71-72 [doi]
• Comparing indirect encodings by evolutionary attractor analysis in the trait space of modular robotsMatteo De Carlo, Eliseo Ferrante, A. E. Eiben. 73-74 [doi]
• A preliminary study towards an improved shepherding modelHeba El-Fiqi, Benjamin Campbell, Saber M. Elsayed, Anthony Perry, Hemant Kumar Singh, Robert Hunjet, Hussein A. Abbass. 75-76 [doi]
• ab initio trading strategies in heterogeneous environmentsDavid Rushing Dewhurst, Yi Li, Alexander Bogdan, Jasmine Geng. 76-84 [doi]
• Safer reinforcement learning through evolved instinctsDjordje Grbic, Sebastian Risi. 77-78 [doi]
• Exploring the BipedalWalker benchmark with MAP-Elites and curiosity-driven A3CVikas Gupta, Nathanaël Aubert-Kato, Leo Cazenille. 79-80 [doi]
• Diversity-based design assist for large legged robotsGerard Howard, Thomas Lowe, Wade Geles. 81-82 [doi]
• Diversity in swarm robotics with task-independent behavior characterizationTanja Katharina Kaiser, Heiko Hamann. 83-84 [doi]
• Novelty search makes evolvability inevitableStéphane Doncieux, Giuseppe Paolo, Alban Laflaquière, Alexandre Coninx. 85-93 [doi]
• Evolving an artificial creoleGregory Furman, Geoff Nitschke. 85-86 [doi]
• The expense of neuro-morpho functional machinesScott Hallauer, Geoff Nitschke. 87-88 [doi]
• Adaptive reinforcement learning through evolving self-modifying neural networksSamuel Schmidgall. 89-90 [doi]
• Divergent search for image classification behaviorsJeremy Tan, Bernhard Kainz. 91-92 [doi]
• Novelty producing synaptic plasticityAnil Yaman, Giovanni Iacca, Decebal Constantin Mocanu, George Fletcher, Mykola Pechenizkiy. 93-94 [doi]
• Covariance matrix adaptation for the rapid illumination of behavior spaceMatthew C. Fontaine, Julian Togelius, Stefanos Nikolaidis, Amy K. Hoover. 94-102 [doi]
• An artificial sequential immune responses model for anomaly detectionWen Zhou, Yiwen Liang. 95-96 [doi]
• Adea - Evolving glyphs for aiding creativity in typeface designDaniel Lopes, João Correia, Penousal Machado. 97-98 [doi]
• Evolving neural network agents to play atari games with compact state representationsAdam Tupper, Kourosh Neshatian. 99-100 [doi]
• Evolving search trajectoriesVincent Hénaux, Adrien Goëffon, Frédéric Saubion. 101-102 [doi]
• Discovering representations for black-box optimizationAdam Gaier, Alexander Asteroth, Jean-Baptiste Mouret. 103-111 [doi]
• An efficient evolutionary solution to the joint order batching - order picking planning problemRiccardo Lucato, Jonas K. Falkner, Lars Schmidt-Thieme. 103-104 [doi]
• QoS-constrained multi-objective distributed data-intensive web service composition - NSGA-II with repair methodSoheila Sadeghiram, Hui Ma 0001, Gang Chen 0002. 105-106 [doi]
• A preliminary approach to evolutionary multitasking for dynamic flexible job shop scheduling via genetic programmingFangfang Zhang, Yi Mei, Su Nguyen, Mengjie Zhang. 107-108 [doi]
• Evolving network structures for text classification using genetic algorithmsHayden Andersen, Xiaoying Gao, Bing Xue 0001, Mengjie Zhang. 109-110 [doi]
• An adaptive neuroevolution-based hyperheuristicEtor Arza, Josu Ceberio, Aritz Pérez, Ekhiñe Irurozki. 111-112 [doi]
• Evolution of distributed neural controllers for voxel-based soft robotsEric Medvet, Alberto Bartoli, Andrea De Lorenzo, Giulio Fidel. 112-120 [doi]
• Multi-objective data stream clusteringMohammed Oualid Attaoui, Hanene Azzag, Mustapha Lebbah, Nabil Keskes. 113-114 [doi]
• A first step toward incremental evolution of convolutional neural networksDustin K. Barnes, Sara R. Davis, Emily M. Hand, Sushil J. Louis. 115-116 [doi]
• Automatically extracting features for face classification using multi-objective genetic programmingYing Bi, Bing Xue 0001, Mengjie Zhang. 117-118 [doi]
• Diversity-driven wide learning for training distributed classification modelsRui P. Cardoso, Emma Hart, Jeremy V. Pitt. 119-120 [doi]
• Quality diversity for multi-task optimizationJean-Baptiste Mouret, Glenn Maguire. 121-129 [doi]
• Turing learning with hybrid discriminators: combining the best of active and passive learningYue Gu, Wei Li 0055, Roderich Groß. 121-122 [doi]
• Evolving deep autoencodersJeffrey Hajewski, Suely Oliveira, Xiaoyu Xing. 123-124 [doi]
• Enabling XCSF to cope with dynamic environments via an adaptive error thresholdTim Hansmeier, Paul Kaufmann, Marco Platzner. 125-126 [doi]
• Towards a Pittsburgh-style LCS for learning manufacturing machinery parametrizationsMichael Heider, David Pätzel, Jörg Hähner. 127-128 [doi]
• The data-driven physical-based equations discovery using evolutionary approachAlexander Hvatov, Mikhail Maslyaev. 129-130 [doi]
• Towards crossing the reality gap with evolved plastic neurocontrollersHuanneng Qiu, Matthew Garratt, David Howard, Sreenatha G. Anavatti. 130-138 [doi]
• An evolution-based approach for efficient differentiable architecture searchMasayuki Kobayashi, Tomoharu Nagao. 131-132 [doi]
• A Multi-objective architecture search for generative adversarial networksMasayuki Kobayashi, Tomoharu Nagao. 133-134 [doi]
• Towards evolving robust neural architectures to defend from adversarial attacksShashank Kotyan, Danilo Vasconcellos Vargas. 135-136 [doi]
• Equilibrium in classification: a new game theoretic approach to supervised learningRodica Ioana Lung, Mihai Alexandru Suciu. 137-138 [doi]
• CPPN2GAN: combining compositional pattern producing networks and GANs for large-scale pattern generationJacob Schrum, Vanessa Volz, Sebastian Risi. 139-147 [doi]
• Improving an evolutionary wrapper for attack detection by including feature importance informationJavier Maldonado, María-Cristina Riff. 139-140 [doi]
• DarwiNN: efficient distributed neuroevolution under communication constraintsGurshaant Singh Malik, Lucian Petrica, Nachiket Kapre, Michaela Blott. 141-142 [doi]
• Generation of consistent sets of multi-label classification rules with a multi-objective evolutionary algorithmThiago Zafalon Miranda, Diorge Brognara Sardinha, Márcio Porto Basgalupp, Yaochu Jin, Ricardo Cerri. 143-144 [doi]
• Searching for activation functions using a self-adaptive evolutionary algorithmAndrew Nader, Danielle Azar. 145-146 [doi]
• BACS: integrating behavioral sequences to ACS2Romain Orhand, Anne Jeannin-Girardon, Pierre Parrend, Pierre Collet. 147-148 [doi]
• Interactive evolution and exploration within latent level-design space of generative adversarial networksJacob Schrum, Jake Gutierrez, Vanessa Volz, Jialin Liu 0001, Simon M. Lucas, Sebastian Risi. 148-156 [doi]
• A genetic programming method for classifier construction and cost learning in high-dimensional unbalanced classificationWenBin Pei, Bing Xue 0001, Lin Shang, Mengjie Zhang. 149-150 [doi]
• Evolutionary super-resolutionBaptiste Rozière, Nathanaël Carraz Rakotonirina, Vlad Hosu, Hanhe Lin, Andry Rasoanaivo, Olivier Teytaud, Camille Couprie. 151-152 [doi]
• Population-based evolutionary distributed SGDAmna Shahab, Boris Grot. 153-154 [doi]
• HyperFDA: a bi-level optimization approach to neural architecture search and hyperparameters' optimization via fractal decomposition-based algorithmLéo Souquet, Nadiya Shvai, Arcadi Llanza, Amir Nakib. 155-156 [doi]
• Evolutionary neural network structure search for DNN pruning and features separationZhaoyang Wu, Lin Lin, Guoliang Gong, Rui Xu, Mitsuo Gen, Yong Zhou. 157-158 [doi]
• A deep learning approach to predicting solutions in streaming optimisation domainsMohamad Alissa, Kevin Sim, Emma Hart. 157-165 [doi]
• An EDA with swarm intelligence for the multi-objective flexible job-shop problemLuiz Carlos Felix Carvalho, Márcia Aparecida Fernandes. 159-160 [doi]
• A generic and computationally efficient automated innovization method for power-law design rulesKanish Garg, Anish Mukherjee, Sukrit Mittal, Dhish Kumar Saxena, Kalyanmoy Deb. 161-162 [doi]
• Framework to select an improved radiographic image using Speed-constrained modified particle swarm optimizationLuhana Aracelli González Zarza, Alan Mathías Ruiz Díaz Nodari, José Luis Vázquez Noguera, Diego Pedro Pinto-Roa. 163-164 [doi]
• An improved multiobjective optimization evolutionary algorithm based on decomposition with hybrid penalty schemeJinglei Guo, Miaomiao Shao, Shouyong Jiang, Shengxiang Yang. 165-166 [doi]
• Dynamic bi-objective routing of multiple vehiclesJakob Bossek, Christian Grimme, Heike Trautmann. 166-174 [doi]
• A local hypervolume contribution schema to improve spread of the pareto front and computational timeEdgar Manoatl López, Carlos Ignacio Hernández Castellanos. 167-168 [doi]
• Time complexity analysis of the dominance degree approach for non-dominated sortingSumit Mishra, Maxim Buzdalov, Rakesh Senwar. 169-170 [doi]
• InnovizationSukrit Mittal, Dhish Kumar Saxena, Kalyanmoy Deb. 171-172 [doi]
• Performance evaluation of the MOEA/D algorithm for the solution of a microgrid planning problemMiguel A. Rodriguez, Dario F. Lopez, Sergio F. Contreras, Camilo A. Cortés, Johanna M. A. Myrzik. 173-174 [doi]
• PaletteStarViz: a visualization method for multi-criteria decision making from high-dimensional pareto-optimal frontA. K. M. Khaled Ahsan Talukder, Kalyanmoy Deb. 175-176 [doi]
• A robust experimental evaluation of automated multi-label classification methodsAlex Guimarães Cardoso de Sá, Cristiano Guimarães Pimenta, Gisele Lobo Pappa, Alex Alves Freitas. 175-183 [doi]
• Using gradient-free local search within MOEAs for the treatment of constrained MOPsLourdes Uribe, Adriana Lara, Kalyanmoy Deb, Oliver Schütze. 177-178 [doi]
• Dynamic Multi-objective Optimisation Problems with Intertemporal DependenciesBernard van Tonder, Mardé Helbig. 179-180 [doi]
• Improving NSGA-III for flexible job shop scheduling using automatic configuration, smart initialization and local searchYali Wang, Bas van Stein, Thomas Bäck, Michael Emmerich. 181-182 [doi]
• A many-objective route planning benchmark problem for navigationJens Weise, Sanaz Mostaghim. 183-184 [doi]
• Do sophisticated evolutionary algorithms perform better than simple ones?Michael Foster 0001, Matthew Hughes, George O'Brien, Pietro S. Oliveto, James Pyle, Dirk Sudholt, James Williams. 184-192 [doi]
• Worst-case conditional hardness and fast algorithms with random inputs for non-dominated sortingSorrachai Yingchareonthawornchai, Proteek Chandan Roy, Bundit Laekhanukit, Eric Torng, Kalyanmoy Deb. 185-186 [doi]
• Solving min-max optimisation problems by means of bilevel evolutionary algorithms: a preliminary studyMargarita Antoniou, Gregor Papa. 187-188 [doi]
• A view of estimation of distribution algorithms through the lens of expectation-maximizationDavid H. Brookes, Akosua Busia, Clara Fannjiang, Kevin Murphy, Jennifer Listgarten. 189-190 [doi]
• Continuous optimization by hierarchical gaussian mixture with clustering embedded resource allocationJhih-wei Chen, Ming-Chun Lu, Tian-Li Yu. 191-192 [doi]
• Solving constrained combinatorial reverse auctions using MOEAs: a comparative studyElaine Guerrero-Peña, Fernanda Nakano Kazama, Paulo de Barros Correia, Aluizio F. R. Araújo. 193-200 [doi]
• Performance2vec: a step further in explainable stochastic optimization algorithm performanceTome Eftimov, Gorjan Popovski, Dragi Kocev, Peter Korosec. 193-194 [doi]
• Look-ahead natural evolutionary strategiesJin Liang Jia, Alfredo Alan Flores Saldivar, Lin Li, Yun Li 0002. 195-196 [doi]
• Parametrized Benchmarking: an outline of the idea and a feasibility studyKarol R. Opara, Anas A. Hadi, Ali Wagdy Mohamed. 197-198 [doi]
• A quantum simulation algorithm for continuous optimizationAnna Ouskova Leonteva, Ulviya Abdulkarimova, Tobias M. Wintermantel, Anne Jeannin-Girardon, Pierre Parrend, Pierre Collet. 199-200 [doi]
• Journey to the center of the linear ordering problemLeticia Hernando, Alexander Mendiburu, José Antonio Lozano. 201-209 [doi]
• Expediting the convergence of evolutionary algorithms by identifying promising regions of the search spaceKamrul Hasan Rahi, Ahsanul Habib, Hemant Kumar Singh, Tapabrata Ray. 201-202 [doi]
• Bayesian CMA-ES: a new approachBenhamou Eric, Saltiel David, Verel Sebastien. 203-204 [doi]
• Differential Evolution with Reversible Linear TransformationsJakub M. Tomczak, Ewelina Weglarz-Tomczak, Ágoston E. Eiben. 205-206 [doi]
• Differential evolution with explicit control of diversity for constrained optimizationGabriel Vázquez, Carlos Segura. 207-208 [doi]
• Impact of additional hardware resources on a parallel genetic algorithmGlen Cancian, Wayne Pullan. 209-210 [doi]
• Solving the single row facility layout problem by differential evolutionPavel Krömer, Jan Platos, Václav Snásel. 210-218 [doi]
• A parallel and distributed multi-population GA with asynchronous migrations: energy-time analysis for heterogeneous systemsJuan José Escobar, Julio Ortega, Antonio Francisco Díaz, Jesús González 0001, Miguel Damas. 211-212 [doi]
• A fast GA for automatically evolving CNN architecturesZhou Hong, Wei Fang, Jun Sun, Xiaojun Wu. 213-214 [doi]
• Efficient machine learning through evolving combined deep neural networksRune Krauss, Marcel Merten, Mirco Bockholt, Saman Fröhlich, Rolf Drechsler. 215-216 [doi]
• SHX: search history driven crossover for real-coded genetic algorithmTakumi Nakane, Xuequan Lu, Chao Zhang. 217-218 [doi]
• Cooperative coevolutionary genetic algorithm using hierarchical clustering of linkage treeTakatoshi Niwa, Koya Ihara, Shohei Kato. 219-220 [doi]
• Multi-layer local optima networks for the analysis of advanced local search-based algorithmsMarcella S. R. Martins, Mohamed El Yafrani, Myriam R. B. S. Delgado, Ricardo Lüders. 219-227 [doi]
• A test problem with difficulty in decomposing into sub-problems for model-based genetic algorithmsKei Ohnishi, Shota Ikeda, Tian-Li Yu. 221-222 [doi]
• Illuminating super mario bros: quality-diversity within platformer level generationOliver Withington. 223-224 [doi]
• Hybrid bayesian evolutionary optimization for hyperparameter tuningLukas Atkinson, Robin Müller-Bady, Martin Kappes. 225-226 [doi]
• Automated design of efficient swarming behaviours: a Q-learning hyper-heuristic approachGabriel Duflo, Grégoire Danoy, El-Ghazali Talbi, Pascal Bouvry. 227-228 [doi]
• Just-in-time batch scheduling subject to batch sizeSergey Polyakovskiy, Dhananjay Thiruvady, Rym M'Hallah. 228-235 [doi]
• Finding a better basis on binary representation through DNN-based epistasis estimationYong Hoon Kim, Yong-Hyuk Kim. 229-230 [doi]
• Parallelized bayesian optimization for problems with expensive evaluation functionsMargarita Alejandra Rebolledo Coy, Frederik Rehbach, A. E. Eiben, Thomas Bartz-Beielstein. 231-232 [doi]
• Evolving genetic programming trees in a rule-based learning frameworkSiddharth Verma, Piyush Borole, Ryan J. Urbanowicz. 233-234 [doi]
• On the Effect of Walsh/Fourier transform in surrogate-assisted genetic algorithmsDong-Pil Yu, Yong-Hyuk Kim. 235-236 [doi]
• Advanced statistical analysis of empirical performance scalingYasha Pushak, Holger H. Hoos. 236-244 [doi]
• Benchmarking parent selection for program synthesis by genetic programmingThomas Helmuth, Amr M. Abdelhady. 237-238 [doi]
• Counterexample-driven genetic programming without formal specificationsThomas Helmuth, Lee Spector, Edward E. Pantridge. 239-240 [doi]
• Transfer Learning of Genetic Programming Instruction SetsThomas Helmuth, Edward E. Pantridge, Grace Woolson, Lee Spector. 241-242 [doi]
• General controllers evolved through grammatical evolution with a divergent searchEnrique Naredo, Conor Ryan, Ivan Guevara, Tiziana Margaria, Paulo Urbano, Leonardo Trujillo. 243-244 [doi]
• Golden parameter search: exploiting structure to quickly configure parameters in parallelYasha Pushak, Holger H. Hoos. 245-253 [doi]
• Image feature learning with a genetic programming autoencoderStefano Ruberto, Valerio Terragni, Jason H. Moore. 245-246 [doi]
• Why and when are loops useful in genetic programming?Anil Kumar Saini, Lee Spector. 247-248 [doi]
• Feature engineering for improving robustness of crossover in symbolic regressionAliyu Sani Sambo, R. Muhammad Atif Azad, Yevgeniya Kovalchuk, Vivek Padmanaabhan Indramohan, Hanifa Shah. 249-250 [doi]
• Refined typed genetic programming as a user interface for genetic programmingPaulo Santos, Sara Silva, Alcides Fonseca. 251-252 [doi]
• A GA for non-uniform sampling harmonic analysisUlviya Abdulkarimova, Igor Santos Peretta, Anna Ouskova Leonteva, Younes Monjid, Rabih Amhaz, Marc Haegelin, Pierre Collet, Christian Rolando. 253-254 [doi]
• Why many travelling salesman problem instances are easier than you thinkSwetha Varadarajan, L. Darrell Whitley, Gabriela Ochoa. 254-262 [doi]
• Beer organoleptic optimisation: utilising swarm intelligence and evolutionary computation methodsMohammad Majid al-Rifaie, Marc Cavazza. 255-256 [doi]
• Multi-objective optimization for worker cross-training: the tri-objective caseAndreas Beham, Viktoria A. Hauder, Johannes Karder, Klaus Altendorfer. 257-258 [doi]
• NGAP: a novel hybrid metaheuristic algorithm for round-trip carsharing fleet planningBoonyarit Changaival, Grégoire Danoy, Dzmitry Kliazovich, Frédéric Guinand, Matthias R. Brust, Jedrzej Musial, Kittichai Lavangnananda, Pascal Bouvry. 259-260 [doi]
• Cable-stayed bridge optimization solution space explorationJoão Correia, Fernando Ferreira, Catarina Maçãs. 261-262 [doi]
• Multi-objective exploration of a granular matter design spaceGary W. Delaney, Gerard Howard. 263-264 [doi]
• Automatic decomposition of mixed integer programs for lagrangian relaxation using a multiobjective approachJake Weiner, Andreas Ernst, Xiaodong Li, Yuan Sun. 263-270 [doi]
• A realistic scooter rebalancing system via metaheuristicsGuilherme Fernandes, Nuno Oliveira, Paulo Cortez, Rui Mendes. 265-266 [doi]
• Wind-turbine design optimization using a many-objective evolutionary algorithmAhsanul Habib, Kamrul Hasan Rahi, Hemant Kumar Singh, Tapabrata Ray. 267-268 [doi]
• Optimising word embeddings with search-based approachesMax Hort, Federica Sarro. 269-270 [doi]
• A new objective function for super-resolution deconvolution of microscopy images by means of a genetic algorithmAxel M. Lacapmesure, Sandra Martínez, Oscar E. Martínez. 271-272 [doi]
• Specific single- and multi-objective evolutionary algorithms for the chance-constrained knapsack problemYue Xie, Aneta Neumann, Frank Neumann 0001. 271-279 [doi]
• A genetic algorithm for matching oil spill particlesHyeon Chang Lee, Hwi-Yeon Cho, Yong-Hyuk Kim. 273-274 [doi]
• An improved brain storm optimization algorithm for fuzzy distributed hybrid flowshop scheduling with setup timeJun-qing Li, Hui Yu, Xiaolong Chen, Wenhan Li, Yu Du, Yu-Yan Han. 275-276 [doi]
• Batch correction of genomic data in chronic fatigue syndrome using CMA-ESAlejandro Lopez Rincon, Aletta D. Kraneveld, Alberto Tonda. 277-278 [doi]
• Effective image clustering using self-organizing migrating algorithmSeyed Jalaleddin Mousavirad, Gerald Schaefer, Iakov Korovin. 279-280 [doi]
• Evolving inborn knowledge for fast adaptation in dynamic POMDP problemsEseoghene Ben-Iwhiwhu, Pawel Ladosz, Jeffery Dick, Wen-Hua Chen, Praveen K. Pilly, Andrea Soltoggio. 280-288 [doi]
• Evolved ensemble of detectors for gross error detectionTien Thanh Nguyen, John McCall, Allan Wilson, Laud Ochei, Helen Corbett, Phil Stockton. 281-282 [doi]
• Real-time genetic optimization of large file transfersHemanta Sapkota, Engin Arslan, Sushil Louis. 283-284 [doi]
• Moving target defense through evolutionary algorithmsErnesto Serrano Collado, Mario García Valdez, Juan Julián Merelo Guervós. 285-286 [doi]
• An application of GA and EDA for passive in-building distributed antenna systemsSiddhartha Shakya, Kin Fai Poon, Khawla AlShanqiti, Anis Ouali, Andrei Sleptchenko. 287-288 [doi]
• Evolving multi-objective ranking models for GMV optimization in E-CommerceAndrew Stanton, Akhila Ananthram, Pablo Crespo. 289-290 [doi]
• Evolutionary optimization of deep learning activation functionsGarrett Bingham, William Macke, Risto Miikkulainen. 289-296 [doi]
• Preliminary study of applied binary neural networks for neural cryptographyRaul Horacio Valencia Tenorio, Chiu-Wing Sham, Danilo Vasconcellos Vargas. 291-292 [doi]
• Towards realistic optimization benchmarks: a questionnaire on the properties of real-world problemsKoen van der Blom, Timo M. Deist, Tea Tusar, Mariapia Marchi, Yusuke Nojima, Akira Oyama, Vanessa Volz, Boris Naujoks. 293-294 [doi]
• Dynamic vessel-to-vessel routing using level-wise evolutionary optimizationYash Vesikar, Julian Blank, Kalyanmoy Deb, Markku Kalio, Alaleh Maskooki. 295-296 [doi]
• Stochastic simulation optimization benchmarking method in consideration of finite period of serviceTakufumi Yoshida, Daisuke Yamaguchi, Keiichi Handa. 297-298 [doi]
• Exploring the evolution of GANs through quality diversityVictor Costa, Nuno Lourenço 0002, João Correia, Penousal Machado. 297-305 [doi]
• Multiobjective direction driven local search for constrained supply chain configuration problemXin Zhang, Zhi-hui Zhan, Jun Zhang. 299-300 [doi]
• Combining sequential model-based algorithm configuration with default-guided probabilistic samplingMarie Anastacio, Holger H. Hoos. 301-302 [doi]
• Handling uncertainty in code smells detection using a possibilistic SBSE approachSofien Boutaib, Slim Bechikh, Carlos A. Coello Coello, Chih-Cheng Hung, Lamjed Ben Said. 303-304 [doi]
• Search-based many-criteria identification of microservices from legacy systemsLuiz Carvalho, Alessandro Garcia, Thelma Elita Colanzi, Wesley K. G. Assunção, Maria Julia de Lima, Baldoino Fonseca, Márcio Ribeiro, Carlos Lucena. 305-306 [doi]
• Feature standardisation and coefficient optimisation for effective symbolic regressionGrant Dick, Caitlin A. Owen, Peter A. Whigham. 306-314 [doi]
• Recommending peer reviewers in modern code review: a multi-objective search-based approachMotaz Chouchen, Ali Ouni 0001, Mohamed Wiem Mkaouer, Raula Gaikovina Kula, Katsuro Inoue. 307-308 [doi]
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• Library for evolutionary algorithms in Python (LEAP)Mark Coletti, Eric O. Scott, Jeffrey K. Bassett. 1571-1579 [doi]
• Integrating heuristiclab with compilers and interpreters for non-functional code optimizationDaniel Dorfmeister, Oliver Krauss. 1580-1588 [doi]
• GA-lapagos, an open-source c framework including a python-based system for data analysisPeter Jamieson, Ricardo S. Ferreira, José Augusto Miranda Nacif. 1589-1590 [doi]
• Implementation matters, also in concurrent evolutionary algorithmsJuan Julián Merelo Guervós, Mario García Valdez, Sergio Rojas Galeano. 1591-1598 [doi]
• Open source evolutionary structured optimizationJérémy Rapin, Pauline Bennet, Emmanuel Centeno, Daniel Haziza, Antoine Moreau, Olivier Teytaud. 1599-1607 [doi]
• High-dimensional multi-level maximum variance threshold selection for image segmentation: a benchmark of recent population-based metaheuristic algorithmsSeyed Jalaleddin Mousavirad, Gerald Schaefer, Zahra Movahedi, Iakov Korovin. 1608-1613 [doi]
• Uncertainty quantification methods for evolutionary optimization under uncertaintyPramudita Satria Palar, Koji Shimoyama, Lavi Rizki Zuhal. 1614-1622 [doi]
• What do you mean?: the role of the mean function in bayesian optimisationGeorge De Ath, Jonathan E. Fieldsend, Richard M. Everson. 1623-1631 [doi]
• A surrogate-assisted GA enabling high-throughput ML by optimal feature and discretization selectionJohan Garcia. 1632-1640 [doi]
• Bayesian methods for multi-objective optimization of a supersonic wing planformTimothy Man Shui Jim, Ghifari Adam Faza, Pramudita Satria Palar, Koji Shimoyama. 1641-1643 [doi]
• Solution approaches for the dynamic stacking problemSebastian Raggl, Andreas Beham, Stefan Wagner 0002, Michael Affenzeller. 1652-1660 [doi]
• Tabu search and iterated greedy for a flow shop scheduling problem with worker assignmentMatheus de Freitas Araujo, José Elias C. Arroyo, Lucas Batista Fialho. 1661-1668 [doi]
• λ, λ)) genetic algorithm for permutationsAnton Bassin, Maxim Buzdalov. 1669-1677 [doi]
• An experimental evaluation of the algebraic differential evolution algorithm on the single row facility layout problemGabriele Di Bari, Marco Baioletti, Valentino Santucci. 1678-1684 [doi]
• On the solvability of routing multiple point-to-point paths in manhattan meshesReitze Jansen, Yannick Vinkesteijn, Daan van den Berg. 1685-1689 [doi]
• dMFEA-II: An adaptive multifactorial evolutionary algorithm for permutation-based discrete optimization problemsEneko Osaba, Aritz D. Martinez, Akemi Gálvez, Andrés Iglesias 0001, Javier Del Ser. 1690-1696 [doi]
• Monte carlo tree search on perfect rectangle packing problem instancesIgor Pejic, Daan van den Berg. 1697-1703 [doi]
• Gradient search in the space of permutations: an application for the linear ordering problemValentino Santucci, Josu Ceberio, Marco Baioletti. 1704-1711 [doi]
• Exploiting multi-objective parallel extremal optimization features in dynamic load balancingIvanoe De Falco, Eryk Laskowski, Richard Olejnik, Umberto Scafuri, Ernesto Tarantino, Marek Tudruj. 1712-1720 [doi]
• cMOGA/D: a novel cellular GA based on decomposition to tackle constrained multiobjetive problemsCosijopii Garcia-Garcia, María-Guadalupe Martínez-Peñaloza, Alicia Morales-Reyes. 1721-1729 [doi]
• An evolutionary approach for constructing multi-stage classifiersNolan H. Hamilton, Errin W. Fulp. 1730-1738 [doi]
• A parallel two-stage genetic algorithm for route planningH. David Mathias, Samantha S. Foley. 1739-1746 [doi]
• ExaEvo: topological optimization and scalability of evolutionary algorithmsIan Bradley Morgan, Daniel R. Tauritz. 1747-1755 [doi]
• An adaption mechanism for the error threshold of XCSFTim Hansmeier, Paul Kaufmann, Marco Platzner. 1756-1764 [doi]
• Investigating exploration techniques for ACS in discretized real-valued environmentsNorbert Kozlowski, Olgierd Unold. 1765-1773 [doi]
• PEPACS: integrating probability-enhanced predictions to ACS2Romain Orhand, Anne Jeannin-Girardon, Pierre Parrend, Pierre Collet. 1774-1781 [doi]
• An overview of LCS research from IWLCS 2019 to 2020David Pätzel, Anthony Stein, Masaya Nakata. 1782-1788 [doi]
• Generic approaches for parallel rule matching in learning classifier systemsLukas Rosenbauer, Anthony Stein, Jörg Hähner. 1789-1797 [doi]
• XCS as a reinforcement learning approach to automatic test case prioritizationLukas Rosenbauer, Anthony Stein, Roland Maier, David Pätzel, Jörg Hähner. 1798-1806 [doi]
• Learning classifier systems: appreciating the lateralized approachAbubakar Siddique, Will N. Browne, Gina M. Grimshaw. 1807-1815 [doi]
• A scikit-learn compatible learning classifier systemRobert F. Zhang, Ryan J. Urbanowicz. 1816-1823 [doi]
• Multi-objective evolutionary GANMarco Baioletti, Carlos Artemio Coello Coello, Gabriele Di Bari, Valentina Poggioni. 1824-1831 [doi]
• Learning to walk - reward relevance within an enhanced neuroevolution approachI. Colucci, G. Pellegrino, Antonio Della Cioppa, Angelo Marcelli. 1832-1840 [doi]
• Mutational puissance assisted neuroevolutionDivya D. Kulkarni, Shivashankar B. Nair. 1841-1848 [doi]
• GEVO-ML: a proposal for optimizing ML code with evolutionary computationJhe-Yu Liou, Xiaodong Wang, Stephanie Forrest, Carole-Jean Wu. 1849-1856 [doi]
• Neuro-evolution using game-driven cultural algorithmsFaisal Waris, Robert G. Reynolds. 1857-1865 [doi]
• Using pagerank to uncover patterns in search behavior induced by the bit flip operatorThomas M. Green, Timothy L. Andersen. 1866-1871 [doi]
• Visual mapping of multi-objective optimization problems and evolutionary algorithmsKohei Yamamoto, Tomoaki Takagi, Keiki Takadama, Hiroyuki Sato. 1872-1879 [doi]
• On the sensitivity analysis of cartesian genetic programming hyper-heuristicLuis Filipe de Araujo Pessoa, Bernd Hellingrath, Fernando Buarque de Lima Neto. 1880-1888 [doi]
• The automated design of local optimizers for memetic algorithms employing supportive coevolutionNathaniel R. Kamrath, Aaron Scott Pope, Daniel R. Tauritz. 1889-1897 [doi]
• Time-dependent automatic parameter configuration of a local search algorithmWeerapan Sae-Dan, Marie-Eléonore Kessaci, Nadarajen Veerapen, Laetitia Jourdan. 1898-1905 [doi]
• Dynamic primitive granularity control: an exploration of unique design considerationsBraden N. Tisdale, Aaron Scott Pope, Daniel R. Tauritz. 1906-1914 [doi]
• A benchmark with facile adjustment of difficulty for many-objective genetic programming and its reference setMakoto Ohki. 1915-1922 [doi]
• An annotated dataset of stack overflow post editsSebastian Baltes, Markus Wagner 0007. 1923-1925 [doi]
• Genetic improvement of software efficiency: the curse of fitness estimationMahmoud A. Bokhari, Markus Wagner 0007, Brad Alexander. 1926-1927 [doi]
• x via software data maintenanceWilliam B. Langdon, Oliver Krauss. 1928-1936 [doi]
• Tuning genetic algorithm parameters using design of experimentsMohsen Mosayebi, Manbir Sodhi. 1937-1944 [doi]
• Optimising the fit of stack overflow code snippets into existing codeBrittany Reid, Christoph Treude, Markus Wagner 0007. 1945-1953 [doi]